Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples

Alvin Chan

doi:10.1109/TNNLS.2021.3072166

Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples

2022-11-01

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DOI：10.1109/TNNLS.2021.3072166

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The ability to read, reason, and infer lies at the heart of neural reasoning architectures. After all, the ability to perform logical reasoning over language remains a coveted goal of Artificial Intelligence. To this end, models such as the Turing-complete differentiable neural computer (DNC) boast of real logical reasoning capabilities, along with the ability to reason beyond simple surface-level matching. In this brief, we propose the first probe into DNC's logical reasoning capabilities with a focus on text-based question answering (QA). More concretely, we propose a conceptually simple but effective adversarial attack based on metamorphic relations. Our proposed adversarial attack reduces DNCs' state-of-the-art accuracy from 100% to 1.5% in the worst case, exposing weaknesses and susceptibilities in modern neural reasoning architectures. We further empirically explore possibilities to defend against such attacks and demonstrate the utility of our adversarial framework as a simple scalable method to improve model adversarial robustness.

Keywords:

Task analysis

Cognition

Plugs

Perturbation methods

Memory modules

Computer architecture

Computational modeling

Adversarial examples

deep learning

differentiable neural computer (DNC)

supervised learning

Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples

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